A Model-Based Approach to Improve Tacrolimus Therapeutic Drug Monitoring in Paediatric Liver Transplantation

Abstract

Introduction: Tacrolimus (TAC) is currently the most widely used immunosuppressive agent in transplantation. Its effectiveness as an immunomodulator is well demonstrated. Moreover its use is characterized by the occurrence of some side-effects, such as nephrotoxicity, and by high pharmacokinetics (PK) variability and risk of drug-drug and drug-food interactions. It is therefore considered as a critical dose-drug with a low therapeutic index drug, which can benefit from blood levels therapeutic drug monitoring (TDM). As frequently performed in paediatric patients, this TDM is based on trough concentrations known to poorly correlate with clinical outcome (graft rejection and TAC toxicity). Moreover, therapeutic ranges were defined based on adult clinical data. Area under the concentrations time curve (AUC) is expected to be a better marker of global systemic exposure to TAC, but is difficult to obtain from classical approach for ethical reasons. The aim of this study is therefore to develop a mathematical model to predict the TAC exposure (AUC) in children based on data collected during clinical routine (C0, proteins, patient age, body weight, body surface area, co-medications, hematocrit, time post-transplantation, etc …). Methods: Nonlinear mixed effect approach was used to develop and validate a model to better characterize TAC PK and therefore routinely predict AUC in children. Retrospective data from 51 pediatric liver transplant patients were used. All patients were primary transplants, aged 0 to 18 years old and receiving living-donor liver transplantations. The study only included patients who had not experienced rejection episode since transplantation and who received TAC as monotherapy. Results: A one compartment model with first order absorption and elimination best described the data. The apparent volume of distribution (Vd) and clearance (CL) were 330 L and 8.4 L/h, respectively and the interindividual variability on Vd and CL were 70% and 37%, respectively. Parameters were estimated with good precision (CV< 20% for all parameters). Hematocrit levels, body weight and time post-transplantation were the covariates retained in the final model that influenced TAC distribution and clearance. An example of predicted (−) and observed (x) concentrations over time is illustrated in graph1.Figure: [Patient TAC concentrations example]Conclusion: This model could be considered as a useful tool to better define the target tacrolimus AUC in paediatrics, currently lacking in the literature, and to optimize AUC-based TDM in children.